Source: College Scorecard, a federally maintained dataset

Outcome Statistics by Cohort:

• Completion Rates disaggregated by Race (percent)
• Post-Graduation Earnings disaggregated by Household Income Terciles (in dollars)

Explanatory Statistics for each school:

• Type (Public, Non-Profit Private, For-Profit Private) (coded 1, 2, 3)
• Instructional Expenditures per Full-time Student (in dollars)

Are "outcome gaps" between groups related to the type of school or expenditures?

• Compute gap metrics as percent difference in outcomes

• Conduct hypothesis testing

Are other variables related to outcome gaps?

• Run Random Forest with additional variables for insight on relative importance

Trend looks generally negative, but with a lot of variance

Interestingly, the gap between middle income and high income students seems to worsen with higher expenditures.

The data looks fairly random. Possibly a negative trend, but fairly weak-looking.

Similar patterns. It does seem that at higher expenditures, there is less variance in completion gaps.

There definitely seem to be differences in means between the three groups, but the distributions are similar.

This correlation for high-low income earnings gaps looks much stronger.

Correlation with high-middle income gaps is less dramatic but still seems to have some negative trend.

This pattern seems fairly flat, maybe a slight positive trend.

Little evidence of significance for the difference in white-black completion rate gap between types of schools.

Here we see evidence that for-profit schools have significantly different white-hispanic completion gaps.

We see a similar pattern with the white-Asian completion gap difference.

Differences looks more significant here.

Less significant in comparison to High-Low.

Differences are less significant. We do see the trends from the past two plots reverse.

Hypothesis: coefficients regressing outcome gaps on expenditures will be negative

Slopes are mostly insignificantly different from zero. Many are positive - Hypothesis rejected

Extremely significant negative coefficient for high-low, others less significant.

Consider relationships and relative importance of other possibly relevant variables, relating to:

• Location
• Student Body Racial Diversity
• Student Body Economic Distribution
• Selectivity
• Type of Degree Awarded

INEXPFTE is quite important compared to other terms. Diversity also seems relevant.

INEXPFTE is important again. Economic variables are more important in these models.

• Completion Rate differences across Races did not seem to correspond strongly with type of school or expenditures

• Signficant evidence of Earnings Gaps across economic backgrounds corresponding with expenditures

• No accounting for multiple testing

• Of many possible variables, Random Forest analysis suggests INEXPFTE is more important than others, but CONTROL is unimportant